Abstract
The progress achieved by location-based services has significantly increased the frequency of access to, and improved the usability of, location information. In a mobile environment, spatial data are utilised to provide various services focused on the location information of users. As spatial data represent location information of various objects, cars, hospitals, personal locations and buildings, they require significant storage space as well as methods for rapid searching and transmission to provide services in a timely manner. In this paper, we propose a bit vector-based compression scheme to reduce the storage space requirements and transmission times for large quantities of spatial data. In the proposed scheme, a bit vector represents the minimum bounding rectangle of an R-tree as a location vector of x- and y-axes in quadrant 1 of a two-dimensional graph and stores each axis utilising 1 byte. This has double the compression effect, as compared to that of a conventional compression scheme that performs compression utilising a maximum of 4 bytes. Storage space is reduced to 12.5%, as compared to conventional compression schemes, and the speed of transmission across the network is increased.
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07045642, NRF-2017R1D1A1B03035884).
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Oh, D., Kim, J. (2019). Bit-Vector-Based Spatial Data Compression Scheme for Big Data. In: Lam, HP., Mistry, S. (eds) Service Research and Innovation. ASSRI ASSRI 2018 2018. Lecture Notes in Business Information Processing, vol 367. Springer, Cham. https://doi.org/10.1007/978-3-030-32242-7_7
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DOI: https://doi.org/10.1007/978-3-030-32242-7_7
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